!pip install pandas
!pip install ggplot
!pip install matplotlib
!pip install seaborn
!pip install adjustText
!pip install numpy
!pip install tkinter
!pip install scipy
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import pandas as pd
from plotnine import *
from matplotlib.axis import Axis
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
import seaborn as sb
import numpy as np
from adjustText import adjust_text
from tkinter import * 
from tkinter import font
from scipy import interpolate

q1 = pd.read_csv("/Users/minhtran/Desktop/GSB544/Lab 1/Data/q1data.csv")

q1
income life_exp population year country four_regions six_regions eight_regions world_bank_region
0 1910.0 61.0 29200000.0 2010 Afghanistan asia south_asia asia_west South Asia
1 11100.0 78.1 2950000.0 2010 Albania europe europe_central_asia europe_east Europe & Central Asia
2 11100.0 74.7 36000000.0 2010 Algeria africa middle_east_north_africa africa_north Middle East & North Africa
3 46900.0 81.9 84500.0 2010 Andorra europe europe_central_asia europe_west Europe & Central Asia
4 7680.0 60.8 23400000.0 2010 Angola africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa
... ... ... ... ... ... ... ... ... ...
192 20400.0 75.4 28400000.0 2010 Venezuela americas america america_south Latin America & Caribbean
193 5350.0 73.3 88000000.0 2010 Vietnam asia east_asia_pacific east_asia_pacific East Asia & Pacific
194 4700.0 67.8 23200000.0 2010 Yemen asia middle_east_north_africa asia_west Middle East & North Africa
195 3200.0 57.5 13600000.0 2010 Zambia africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa
196 2560.0 54.4 12700000.0 2010 Zimbabwe africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa

197 rows × 9 columns

The aesthetics being used are:

Bubble Plot

#Style the seaborn theme
sb.set_theme()
sb.set_style('whitegrid')
colors = ["#FF798F", "#7FEB00", "#00D5E9", "#FFE600"]
sb.set_palette(sb.color_palette(colors))

#Set notebook's figure size
plt.rcParams['figure.figsize'] = [60, 40]


#Set up bubble plot
q1_graph = sb.scatterplot(data = q1,
               x = "income",
               y = "life_exp",
               size = "population",
               legend = False,
               alpha = 0.9,
               hue = "four_regions",
               edgecolor = 'black',
               linewidth = 2,
               sizes = (500, 50000),
               zorder = 3)


#Scale x-axis & y-axis

x_ticks = [500, 1000, 2000, 4000, 8000, 16000, 32000, 64000, 128000]
x_labels = [str(x) for x in x_ticks]
q1_graph.set_xscale('log')
q1_graph.set_xticks(x_ticks)
q1_graph.set_xticklabels(x_labels, size = 30)


y_ticks = [0, 20, 30, 40, 50, 60, 70, 80, 90]
y_labels = [str(x) for x in x_ticks]


q1_graph.set_yticks(y_ticks)
q1_graph.set_yticklabels(y_labels, size = 30)






#Add 2022 Watermark & Income Level

q1_graph.text(0.5, 0.5, '2 0 1 0',
              transform = q1_graph.transAxes,
              fontsize = 400, 
              color='silver', 
              alpha=0.5,
              ha='center', 
              va='center',
              zorder = 1)

q1_graph.text(0.5, 0.98, "INCOME LEVEL 1     LEVEL 2     LEVEL 3     LEVEL 4",
              transform = q1_graph.transAxes,
              fontsize = 50, 
              color='gray', 
              alpha=0.5,
              ha='center', 
              va='top')


#Add annotations



#Add labels

plt.xlabel("Income",
           fontweight = "bold",
           font = "Sans Serif",
           color = "darkslategray", 
           fontsize = '50', 
           horizontalalignment='center')

plt.ylabel("Life Expectancy", 
           fontweight = "bold",
           font = "Sans Serif",
           color = "darkslategray", 
           fontsize = '50', 
           verticalalignment='center',
           labelpad = 30)
    
#Show the graph
plt.show()

# Define color palette
colors = ["#FF798F", "#7FEB00", "#00D5E9", "#FFE600"]

# Filter the data to exclude "nan" values in the "four_regions" variable
q1_filtered = q1.dropna(subset=['four_regions'])

# Create the Plotnine plot
plot1a = (ggplot(q1_filtered, aes(x="four_regions", y="income", fill="four_regions"))
        + geom_boxplot()
        + scale_fill_manual(values=colors)  # Set color palette
        + labs(x="Regions", y="Income")
        + scale_y_log10()  # Set y-axis scale to logarithmic
        + theme_minimal()  # Set theme to minimal
        + theme(figure_size=(25, 15))  # Adjust figure size
        + theme(text=element_text(size=20), axis_title=element_text(size=30, face="bold"), legend_title=element_text(size=20))  # Adjust text and axis label sizes
        + theme(panel_grid_major=element_text(color="gray", alpha=0.5))  # Add gridlines
        + labs(fill="Regions")
        + theme(legend_position=(0.75, 0.9))  # Move legend to the right
        )


plot1b = (ggplot(q1_filtered, aes(x="four_regions", y="life_exp", fill="four_regions"))
        + geom_boxplot()
        + scale_fill_manual(values=colors)  # Set color palette
        + labs(x="Regions", y="Life Expectancy")
        + scale_y_log10()  # Set y-axis scale to logarithmic
        + theme_minimal()  # Set theme to minimal
        + theme(figure_size=(25, 15))  # Adjust figure size
        + theme(text=element_text(size=20), axis_title=element_text(size=30, face="bold"), legend_title=element_text(size=20))  # Adjust text and axis label sizes
        + theme(panel_grid_major=element_text(color="gray", alpha=0.5))  # Add gridlines
        + labs(fill="Regions")
        + theme(legend_position=(0.2, 0.9))  # Move legend to the left
        )

plot1c  = (ggplot(q1_filtered, aes(x="four_regions", y="population", fill="four_regions"))
        + geom_boxplot()
        + scale_fill_manual(values=colors)  # Set color palette
        + labs(x="Regions", y="Population")
        + scale_y_log10()  # Set y-axis scale to logarithmic
        + theme_minimal()  # Set theme to minimal
        + theme(figure_size=(25, 15))  # Adjust figure size
        + theme(text=element_text(size=20), axis_title=element_text(size=30, face="bold"), legend_title=element_text(size=20))  # Adjust text and axis label sizes
        + theme(panel_grid_major=element_text(color="gray", alpha=0.5))  # Add gridlines
        + labs(fill="Regions")
        + theme(legend_position=(0.2, 0.9))  # Move legend to the left
        )

# Show the plot
print(plot1a)
print(plot1b)
print(plot1c)





/Users/minhtran/anaconda3/lib/python3.11/site-packages/plotnine/layer.py:284: PlotnineWarning: stat_boxplot : Removed 2 rows containing non-finite values.

This is a spectacular graph that is clearly superior to the other for the following reasons: Pros: -Each region is organized into neat and beautiful different bars, because of that, you don’t have to count the individual bubbles so that allows for ease of interpretation. -Outliers are organized in a cleaner manner so you don’t have to stretch your plot out to include them like how we did in the other graph. -We can see the relationship between income, population, and life expectancy for each region.

Cons: -The relationships are not reprensented in one graph like the bubble plot.

q2 = pd.read_csv("/Users/minhtran/Desktop/GSB544/Lab 1/Data/q2data.csv")
q2
imports exports energy year country four_regions six_regions eight_regions world_bank_region
0 NaN NaN NaN 1997 Afghanistan asia south_asia asia_west South Asia
1 36.7 11.5 427 1997 Albania europe europe_central_asia europe_east Europe & Central Asia
2 22.5 22.6 825 1997 Algeria africa middle_east_north_africa africa_north Middle East & North Africa
3 NaN NaN NaN 1997 American Samoa NaN NaN NaN NaN
4 NaN NaN 434 1997 Angola africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa
... ... ... ... ... ... ... ... ... ...
201 52.2 44.8 340 1997 Vietnam asia east_asia_pacific east_asia_pacific East Asia & Pacific
202 NaN NaN NaN 1997 Virgin Islands (U.S.) NaN NaN NaN NaN
203 NaN NaN 241 1997 Yemen NaN NaN NaN NaN
204 31.6 24.6 617 1997 Zambia africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa
205 45.1 43.4 845 1997 Zimbabwe africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa

206 rows × 9 columns

The aesthetics being used are:

Bubble Plot

#Style the seaborn theme
sb.set_theme()
sb.set_style('whitegrid')
colors = ["#FF798F", "#7FEB00", "#00D5E9", "#FFE600"]
sb.set_palette(sb.color_palette(colors))

#Set notebook's figure size
plt.rcParams['figure.figsize'] = (30, 20)

#Set up bubble plot

q2_graph = sb.scatterplot(data = q2,
                         x = "exports",
                         y = "imports",
                         size = "energy",
                         legend = False,
                         alpha = 0.9,
                         hue = "four_regions",
                         edgecolor = "black",
                         linewidth = 2,
                         sizes = (10, 5000),
                         zorder = 3)


#Scale x-axis & y-axis
x_ticks = [20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220]
x_labels = [str(x) for x in x_ticks]

q2_graph.set_xticks(x_ticks)
q2_graph.set_xticklabels(x_labels, size = 30)


y_ticks = [-10 ,50, 100, 150, 200]
y_labels = [str(y) for y in y_ticks]

q2_graph.set_yticks(y_ticks)
q2_graph.set_yticklabels(y_labels, size = 30)

#Add 2022 Watermark & Income Level

q2_graph.text(0.5, 0.5, '1 9 9 7',
              transform = q2_graph.transAxes,
              fontsize = 450, 
              color='silver', 
              alpha=0.5,
              ha='center', 
              va='center',
              zorder = 1)




#Add label

plt.xlabel("Exports",
           fontweight = "bold",
           font = "Sans Serif",
           color = "darkslategray", 
           fontsize = '35', 
           horizontalalignment='center')

plt.ylabel("Imports", 
           fontweight = "bold",
           font = "Sans Serif",
           color = "darkslategray", 
           fontsize = '35', 
           verticalalignment='center',
           labelpad = 30)
Text(0, 0.5, 'Imports')



# Create the histogram plot
plot = ggplot(q2, aes(x='exports')) + \
    geom_histogram(binwidth=10, fill='#FF798F', color='black', alpha=0.9) + \
    scale_x_continuous(breaks=[20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220]) + \
    scale_y_continuous(breaks=[0, 2, 4, 6, 8, 10], limits=(0, 10)) + \
    labs(x="Exports", y="Imports") + \
    theme_minimal() + \
    theme(text=element_text(size=20),
          axis_text_x=element_text(size=15),
          axis_text_y=element_text(size=15),
          axis_title_x=element_text(size=25, face="bold", color="darkslategray"),
          axis_title_y=element_text(size=25, face="bold", color="darkslategray"),
          panel_grid_major=element_blank(),
          panel_grid_minor=element_blank())

# Display the plot
print(plot)

# Save the plot to a file (optional)
# plot.save("histogram_plot.png", dpi=300)

# Show the plot (optional)
# plot.draw()
# plt.show()
/Users/minhtran/anaconda3/lib/python3.11/site-packages/plotnine/layer.py:284: PlotnineWarning: stat_bin : Removed 35 rows containing non-finite values.
/Users/minhtran/anaconda3/lib/python3.11/site-packages/plotnine/layer.py:364: PlotnineWarning: geom_histogram : Removed 5 rows containing missing values.

This plot is not a good plot because it failed to show the relationship between imports and exports. As we can see here, it is ineffective because it was only able to show exports and its frequency at best. Furthermore, it also failed to incorporate to other important variables, the continent (represented by different colors in the bubble graph) and the energy (represented by the different sizes of the bubble graph).

q3 = pd.read_csv("/Users/minhtran/Desktop/GSB544/Lab 1/Data/q3data.csv")
q3
internet_users gdp income year country four_regions six_regions eight_regions world_bank_region
0 0.00456 330.0 1190.0 2001 Afghanistan asia south_asia asia_west South Asia
1 0.39000 2570.0 6780.0 2001 Albania europe europe_central_asia europe_east Europe & Central Asia
2 1.59000 3770.0 9230.0 2001 Algeria africa middle_east_north_africa africa_north Middle East & North Africa
3 NaN 10500.0 NaN 2001 American Samoa NaN NaN NaN NaN
4 11.30000 42600.0 35600.0 2001 Andorra europe europe_central_asia europe_west Europe & Central Asia
... ... ... ... ... ... ... ... ... ...
210 1.85000 847.0 3270.0 2001 Vietnam asia east_asia_pacific east_asia_pacific East Asia & Pacific
211 27.50000 36700.0 NaN 2001 Virgin Islands (U.S.) NaN NaN NaN NaN
212 0.51900 1190.0 4940.0 2001 Yemen asia middle_east_north_africa asia_west Middle East & North Africa
213 0.47800 991.0 2080.0 2001 Zambia africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa
214 1.10000 1330.0 3190.0 2001 Zimbabwe africa sub_saharan_africa africa_sub_saharan Sub-Saharan Africa

215 rows × 9 columns

The aesthetics being used are:

Bubble Plot

#Style the seaborn theme
sb.set_theme()
sb.set_style('whitegrid')
colors = ["#FF798F", "#7FEB00", "#00D5E9", "#FFE600"]
sb.set_palette(sb.color_palette(colors))

#Set notebook's figure size
plt.rcParams['figure.figsize'] = (30, 20)

#Set up bubble plot

q3_graph = sb.scatterplot(data = q3,
                         x = "internet_users",
                         y = "gdp",
                         size = "income",
                         legend = False,
                         alpha = 0.9,
                         hue = "four_regions",
                         edgecolor = "black",
                         linewidth = 2,
                         sizes = (60, 6000),
                         zorder = 3)

#Add 2022 Watermark & Income Level

q3_graph.text(0.5, 0.5, '2 0 0 1',
              transform = q3_graph.transAxes,
              fontsize = 450, 
              color='silver', 
              alpha=0.5,
              ha='center', 
              va='center',
              zorder = 1)

#Style fonts for x-axis & y-axis labels

x_ticks = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
x_labels = [str(x) for x in x_ticks]


q1_graph.set_xticks(x_ticks)
q1_graph.set_xticklabels(x_labels, size = 30)


y_ticks = [200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000]
y_labels = [str(y) for y in y_ticks]

q3_graph.set_yscale('log')
q3_graph.set_yticks(y_ticks)


q3_graph.set_xticklabels(x_labels, size = 30)
q3_graph.set_yticklabels(y_labels, size = 30)
#Add labels

plt.xlabel("Individuals using the Internet",
           fontweight = "bold",
           font = "Sans Serif",
           color = "darkslategray", 
           fontsize = '35', 
           horizontalalignment='center',
           labelpad = 20)

plt.ylabel("GDP/capita", 
           fontweight = "bold",
           font = "Sans Serif",
           color = "darkslategray", 
           fontsize = '35', 
           verticalalignment='center',
           labelpad = 40)
/var/folders/6w/jjc81v4j2yd2ccrn72bgbl3r0000gn/T/ipykernel_18930/265318195.py:52: UserWarning: FixedFormatter should only be used together with FixedLocator
Text(0, 0.5, 'GDP/capita')


q3_filtered = q3.dropna(subset=['four_regions'])


# Create plotnine object



plot = (
    ggplot(q3_filtered, aes(x='internet_users')) +
    geom_density(aes(fill='four_regions'), alpha=0.5, bw=5) +  # Adjust the bw parameter
    scale_fill_manual(values=["#FF798F", "#7FEB00", "#00D5E9", "#FFE600"]) +
    theme_minimal() +
    labs(
        x="Individuals using the Internet",
        y="Density",
        title="Density Plot",
    ) +
    theme(
        text=element_text(size=15),
        axis_text=element_text(size=12),
        axis_title=element_text(size=15),
        legend_position='none'
    )
)
# Display the plot
print(plot)
/Users/minhtran/anaconda3/lib/python3.11/site-packages/plotnine/layer.py:284: PlotnineWarning: stat_density : Removed 7 rows containing non-finite values.

Compared to the bubble plot, this plot is ineffective because it failed to show the relationship between individual using the internet and the GDP for each of the regions